2006:45

M A S T E R ' S T H E S I S

Factors Influencing Adoptionof Online Ticketing

Mitra Karami

Luleå University of Technology

Master Thesis, Continuation Courses Marketing and e-commerce

Department of Business Administration and Social SciencesDivision of Industrial marketing and e-commerce

2006:45 - ISSN: 1653-0187 - ISRN: LTU-PB-EX--06/45--SE

Abstract:

This thesis attempts to analyze the factors that affect the intention to

purchase train tickets through internet. Technology acceptance model was

chosen as the basis of framework of this study to explain passengers`

acceptance through their intentions to buy tickets online and to rationalize their

intentions in terms of attitude, perceived usefulness, and perceived ease of use,

subjective norms, perceived behavioral control and trust. Survey was conducted

to gather the data. The measures and hypotheses were analyzed using partial

least square technique. Results show that social factors, perceived behavioral

control, attitude and trust significantly influence passengers` intention towards

adopting internet ticketing. The implications of the findings for theory and practice

are discussed.

Key words: e-commerce, Adoption of information Technology, online ticketing, Theory of reasoned action, Theory of planned behavior

1

Acknowledgements:

Few people are as fortunate as I have been; benefited from two of the best

supervisors; during doing this post graduate thesis. I would like to express my

sincere gratitude to my Luth supervisor, Dr. Limayem, for being very supportive

and helpful during the work process of this thesis. Also, I am also deeply grateful

to my TMU supervisor, Dr. Sepehri, for his encouragement, guidance and

invaluable comments on this thesis. He spent numerous efforts in advising me

with invaluable suggestions throughout this study. Without their assistance this

thesis would never be completed. Finally, special thanks to my family for their

support and encouragement throughout my life.

January, 2006 Mitra Karami

2

Table of Contents: 1. CHAPTER ONE: INTRODUCTION ..................................................................7

1.1 Introduction.........................................................................................7

1.2 Background ........................................................................................9

1.2.1 Online ticketing .......................................................................... .10

1.2.2 Online ticketing in Iran ............................................................... .11

1.3 problem discussion and justification ................................................ .12

1.4 problem statement............................................................................13

1.5 research question............................................................................ .14

1.6 purpose of the research.................................................................. ..14

1.7 disposition of the thesis ................................................................... .14

2. CHAPTER TWO: LITERATURE REVIEWE.................................................. .16

2.1 Literature Review............................................................................ .16

2.1.1 Attitude...................................................................................... .18

2.1.2 Intention to shop online............................................................. .18

2.1.3 Perceived usefulness................................................................ .18

2.1.4 Perceived ease of use .............................................................. .19

2.1.5 Subjective norm ........................................................................ .19

2.1.6 Perceived behavioral control..................................................... .19

2.1.7 Trust.......................................................................................... .20

2.1.8. Internet usage...........................................................................21

2.1.9 Enjoyment ................................................................................. .21

2.1.10 Perceived Risk .........................................................................21

2.1.11 Experience.............................................................................. .22

2.1.12 Innovativeness .........................................................................22

2.1.13 Habit ....................................................................................... .23

2.1.14 Perceived consequences........................................................ .23

2.1.15 Demographic variables ........................................................... .24

3

2.2 Theoretical framework .................................................................... .24

2.3 Adoption theories............................................................................ .25

2.3.1 Theory of reasoned action………………………………………….25

2.3.2 Theory of planned behavior ...................................................... .26

2.3.2 Technology acceptance model ................................................. .30

2.4 Difference between theories........................................................... .33

2.5 Conceptual model and hypotheses ................................................ .34

2.6 Pilot study....................................................................................... .36

2.7 Description of the research hypotheses ......................................... .38

2.7.1 Attitude...................................................................................... .39

2.7.2 Perceived ease of use .............................................................. .39

2.7.3 Perceived usefulness................................................................ .39

2.7.4 Subjective norm ........................................................................ .40

2.7.5 Perceived behavioral control..................................................... .41

2.7.6 Trust.......................................................................................... .42

2.7.8 Behavioral intention .................................................................... .42

3. CHAPTER THREE: RESEARCH METHODOLOGY..................................... .44 3.1 Research purpose……………………………………………………..…44

3.1.1 Exploratory research................................................................ .45

3.1.2 Descriptive research ................................................................ .45

3.1.3 Explanatory research ............................................................... .46

3.2 Research approach ........................................................................ .46

3.3 Deductive versus inductive............................................................. .47

3.4 Research strategy .......................................................................... .48

3.5 Defining target population............................................................... .50

3.6 Sampling technique selection......................................................... .52

3.7 Questionnaire development............................................................ .52

3.8 Data collection................................................................................ .54

4

4. CHAPTER FOUR: DATA ANALYSIS ........................................................... .55 4.1 Data analysis method ..................................................................... .55

4.2 Validity and reliability ...................................................................... .56

4.3 Results ........................................................................................... .58

4.3.1 Antecedents of intention ........................................................... .59

4.3.2 Antecedents of attitude ............................................................. .61

4.3.3 Antecedents of perceived usefulness ....................................... .62

5. CHAPTER FIVE : FINDINGS AND CONCLUSION....................................... .64

5.1 Implications for the theory .............................................................. .64

5.2 Innovative part of the research. ...................................................... .65

5.3 Discussion ...................................................................................... .65

5.4 Conclusion and further research .................................................... .67

REFERENCES .................................................................................................. .69

Appendix A. Acronyms ................................................................................... .76

Appendix B. Questionnaire............................................................................. .77

Appendix C. Comparative analysis between techniques................................ .81

Appendix D. Compatibility by Research Approach ......................................... .82

5

LIST OF TABLES Table 2.1: Determinants of online shopping ....................................................17

Table 3.1: Relevant Situations for Different Research Strategies................... .49

Table 3.2: Research variable and measurements .......................................... .53

Table 4.1: Weights and loadings .....................................................................57

Table 4.2: Composite reliability ...................................................................... .58

Table 4.3 Results of the hypotheses tests...................................................... .62

LIST OF FIGURES

Figure 1.1: Research structure ....................................................................... .15

Figure 2.1: Theory of reasoned action............................................................ .27

Figure 2.2: Theory of planned behavior.......................................................... .29

Figure 2.3: Technology acceptance model......................................................31

Figure 2.4: Research model ............................................................................38

Figure 4.1: Results of the hypotheses tests ................................................... .60

6

Chapter One Introduction and Research Problem 1. Introduction and Research Problem In the first chapter, an introduction and a background of this research will be presented. Subsequently research problem and the disposition of the research structure are reported. 1.1 Introduction

Electronic commerce has become one of the essential characteristics in the

Internet era. According to UCLA Center for Communication Policy (2001), online

shopping has become the third most popular internet activity, immediately following e-

mail using/instant messaging and web browsing. It is even more popular than seeking out

entertainment information and news, two commonly thought of activities when

considering what Internet users do when online.

7

Online shopping behavior (also called online buying behavior and Internet

hopping/buying behavior) refers to the process of purchasing products or services via the

Internet.Recent advances in technology, particularly in the field of electronics and

telecommunications, have led business and commerce in new directions over the last few

decades. New forms of trade have emerged from these advances and one area is of

particular interest: Electronic Commerce. Electronic Commerce (EC) has emerged as the

most important way of doing business for years to come. This term was first used by

Kalakota and Whinston (1996). Electronic commerce deals with the facilitation of

transactions and selling of products and services online, i.e. via the internet or any other

telecommunication network. This involves the electronic trading of physical and digital

goods, quite often encompassing all the trading steps such as online marketing, online

ordering, and electronic payment and for digital goods, online distribution (Jelassi, 2005).

This field incorporates a large number of techniques for conducting business

using electronic assistance. By far the most exciting and versatile part of electronic

commerce involve transactions over the Internet According to the United States

Department of Commerce, for the year 2001, total retail sales was US$ 3.50 trillion and

e-commerce retail sales was US$ 32.57 billion (Vijayasarathy, 2004).Electronic

Commerce has been proven to be beneficial to sellers and buyers alike. Through the

usage of electronic commerce, sellers can now access narrow market segments that may

be widely distributed geographically, thereby extending accessibility globally (Napier,

2001).Buyers reap the benefits from having access to global markets and access to a

much larger product catalogs from a wider and varied range of sellers.Kalakota and

Whinstone state that EC has two distinct forms: Business-to-business and business-to

consumer. Much of the growth in revenues from transactions over the Internet has been

achieved from business-to-business exchanges leading to the accumulation of an

impressive body of knowledge and expertise in the area of business-to-business electronic

commerce (Butler and Peppard, 1998).

8

Unfortunately; this is not the case for business-to-consumer EC. With the

exception of software, hardware, travel services, and few other niche areas, shopping on

the Internet is far from universal even among people who spend long hours online.

Moreover, many companies already practicing electronic commerce are having a difficult

time generating satisfactory profits. For example, many e-companies such as

Amazon.com have successfully attracted much attention but have not been able to

convert their competitive advantage into tangible profit (Yan and Parad, 1999).

Selling in cyberspace is very different from selling in physical markets, and it

requires a critical understanding of consumer behavior and how new technologies

challenge the traditional assumptions underlying conventional theories and models.

Butler and Peppard (1998), for example, explain the failure of IBM’s sponsored Web

shopping malls by the naive comprehension of the true nature of consumer behavior on

the net.

A critical understanding of this behavior in cyberspace, as in the physical world,

can not be achieved without a good appreciation of the factors affecting the purchase

decision. Although text books and articles on internet marketing and online consumer

behavior have begun to appear, however comparatively little is known about how web

purchase behavior differs from traditional purchase behavior and whether there are any

specific web-based factors that should taken into account (Heijden et al., 2001).

1.2 Background

Since the focus of this paper is on identifying the factors that influence the

adoption of online ticketing in Iran, thus a brief explanation on online ticketing and its

situation in Iran is in order.

9

1.2.1 Online Ticketing

Electronic ticketing over the Internet is a good example of Internet commerce.

The aim is to facilitate the buying or reservation of tickets online, thereby making the

process more easily accessible and convenient. Through these services tickets may be

purchased from any location and at any time, provided an Internet connection exists.

Typically, the tickets are ordered from a web site that provides both tickets information

and the purchasing or reservation service. Internet or 'online' ticketing is all about

providing a useful and efficient service to clients and customers. The aim is to make the

purchase or reservation of tickets easier. Naturally, this will encourage sales. Online

ticketing system has been used especially by firms who sell travel tickets, performing

arts, game tickets, concerts, movies and many other activities.

The use of the Internet makes buying a ticket more convenient since the service is

available at any geographical location, including your home (or even remotely via a

laptop and cellular phone) and at any time of the day, any day of the year. Online ticket

services have a further advantage by providing relevant information alongside the

service. This can aid purchasing decisions and may encourage future usage (Buford,

1998). So ticket buyers have quite an easy commute to the ticket booth these days-they

only have to get to their home personal computer and onto the internet. It beats standing

in lines (perhaps out in the rain) and day, and the only traffic one encounters is that of the

so-called information superhighway.

There are also benefits for those providing the service. New markets are being

created and ticket sales are increased. Apart from maintenance and data updates, no

manpower is required to provide the service once it has been established. The process of

recording the transactions is more automated and overhead is reduced. An important

point is that ticket providers are also providing a convenient service to customers and are

thereby improving public image and encouraging return customers. (Burford, 1998).

10

Several countries across the globe are already enjoying the benefits of electronic

ticketing including the US, Canada, Australia, New Zealand, Britain, France, Mexico,

Central America, Chile, Argentina, Belgium, Venezuela and The Netherlands. In fact in

the US it has 80 per cent market penetration while in Europe it is approximately 40 per

cent. More than $350 million dollars in event tickets were sold online during 2000 in

U.S.A and the number was increased to $3.9 billion in 2004 (Bhatia, 2004).

1.2.2 Online Ticketing in Iran

In recent years with the support of the Iranian government towards IT plans,

useful steps have been taken in this field. For instance we can refer to the possibility of

payment of the water and the electricity bills from internet and also of selling online train

tickets for the first time in our country. All of these indicate the gradual growth and

development in the IT field in Iran.

Raja Train Company with establishment of the internet ticketing system to sell

tickets online has taken the first step in Iranian economy in the IT field. This company

was pioneer among those companies who wanted to enter the virtual world practically.

The internet ticketing system which is the first step taken in the e-commerce field in Iran

was established with the efforts of Iranian experts in 22 of august 2004.Iranian

passengers by buying the Saman prepaid card and connecting to the raja site

(www.raja.ir), can register in the online ticketing system and purchase train tickets

online. Purchasing tickets through internet, not only reduces the travels inside the city,

but also saves passengers’ times.

11

By the time being only 10% of the total number of tickets are sold online, but if

the demand for buying tickets through the internet increases, the capacity will be

increased. So far the record of the online ticketing system for selling tickets has been 45

tickets each second (Iranian association of rail transport engineering, 2005).

1.2 Problem Discussion and Justification

Selling in cyberspace, however, is very different from selling in physical markets

and requires a critical understanding of online consumer behavior and how new

technologies challenge the traditional assumptions underlying conventional theories and

models (Limayem et al., 2000).

Online consumer behavior is defined as activities directly involved in obtaining,

consuming, and disposing of products and services online, including the decision

processes that precede and follow these actions (Engel et al., 1995). Butler and Peppard

(1998), for example, explain the failure of IBM’s sponsored Web shopping malls by the

naïve comprehension of the true nature of consumer behavior on the net. Online

consumer behavior is an emerging research area with an increasing number of

publications per year. The research articles appear in a variety of journals and conference

proceedings in the fields of Information Systems, Marketing, Management and

Psychology.

Though researchers have made noticeable progress with respect to the scope,

quality and quantity of research, there are still significant Disagreements about the

findings in this area, and the research results appear to be rather Fragmented (Llimayem

et al., 2003).this indicates the lack of good understanding of the factors affecting

consumers’ decision to buy from the Web.

12

Butler and Peppard (1998) eloquently express the need for such Understanding:

“Whether in the cyber-world or the physical world, the heart of marketing

management is understanding consumers and their behavior patterns.”

This lack of understanding caused a wide confusion regarding what is really

happening, how much potential there is, and what companies should be doing to take

advantage of online shopping. As a result, commerce on the Net has turned out to be

baffling, even to experienced managers and marketers (Aldridge et al., 1997).

Critical understanding of consumer behavior in cyberspace, as in the physical

world, cannot be achieved without a good appreciation of the factors affecting the

purchase decision. If cyber marketers know how consumers make these decisions, they

can adjust their marketing strategies to fit this new way of selling in order to convert their

potential customers to real ones and then to retain them. Similarly, Web site designers,

who are faced with the difficult question of how to design pages to make them not only

popular but also effective in increasing sales, can benefit from such an understanding

(Limayem et al., 2000).

1.4 Problem Statement

The above discussion leads us to identify the following research statement:

To gain a better understanding of the online consumer behavior in Iran, that will

result in gaining knowledge regarding the factors that affect the Iranian consumers to

purchase goods and services through internet in general and specifically buying tickets

through internet.

13

1.5 Research Question

The emerged research question is:

What are the main factors that influence the Iranian passengers’ intention to

purchase tickets through internet?

We propose hypothesis testing in trying to find answers to our research question.

Through literature review we will try to make a proper model to identify factors affecting

the intention to purchase tickets through internet. Identification of such factors will shed

light to the online consumer behavior in our country, Iran.

1.6 Purpose of the Research

The purpose of this research is to identify antecedents of intention to purchase

tickets through internet in Iran with the help of behavioral theories. The lack of such

understanding may cause a wide confusion regarding what is really happening, how much

potential there is, and what companies should be doing to take advantage of online

ticketing (Aldridge et al., 1997).

1.7 Disposition of the Thesis

The research paper consists of five chapters; as shown in figure 1.in the first

chapter, introduction, background, research problem and research question is presented.

The second chapter consists of the literature review, theoretical framework and the

research model.

14

In chapter three the methodology used in this study will be explained. In chapter

four data analysis and results will be reported .finally, discussion, conclusion and further

research will be presented in chapter five.

Introduction

Theoretical Review

Research Methodology

Analysis and Results

Discussion and Conclusion

Figure 1.1: Research Structure

15

Chapter Two Theoretical Review 2. Theoretical Review In this chapter we will review the literature concerning the online consumer behavior. We will continue by presenting the popular behavioral theories such as TRA, TPB and TAM .finally, the purposed research model for the adoption of the online ticketing will be presented. 2.1 Literature Review

Online consumer behavior is an emerging research area with an increasing

number of publications per year. The research articles appear in a variety of journals and

conference proceedings in the fields of Information Systems, Marketing, Management,

and Psychology. Though researchers have made noticeable progress with respect to the

scope, quality and quantity of research, there are still significant disagreements about the

findings in this area, and the research results appear to be rather fragmented (Limayem et

al., 2000).

16

Here we try to review the results of the researches that have been conducted

regarding the three main variables of online shopping, namely: attitude toward online

shopping, intention to shop online and online shopping behavior. Table 2.1 shows the

summary of the determinants of attitude toward online shopping, intention to shop online

and online shopping behavior.

Table 2.1. Determinants of Online Shopping

Determinants of Determinants of online Determinants of attitude

Intention to shop online shopping behavior toward online shopping

Attitude Innovativeness Trust

Perceived usefulness Experience Experience

Innovativeness Intention Perceived usefulness

Perceived behavioral control Internet usage Ease of use

Risk Perceived Risk Perceived risk

Social Norm Enjoyment Habit

Experience Perceived behavioral control Innovativeness

Perceived Consequences Demographic variables

Ease of Use

Habit Source: Limayem et al., 2000

The definition of the determinants of intention to shop online, online shopping

behavior, attitudes toward online shopping and summary or the findings of the researches

are in order:

17

2.1.1 Attitude

Attitude refers to one’s evaluation about the consequences of performing a

behavior (Athiyaman, 2002). Consistent with the findings of most IT adoption research, a

significant number of studies found that attitude is a significant antecedent of intention to

shop online (e.g., Athiyaman , 2002; Chen et al., 2002;Frini and Limayem 2000;George

2002).

2.1.2 Intention to Shop Online

Intention to shop online refers to the likelihood that a consumer actually buys

online (Chen et al., 2002).Although this variable is frequently treated as a dependent

variable, several researchers found it to be an important determinant of online shopping

behavior (e.g., Chen et al., 2002; George, 2002; Goldsmith and Goldsmith 2002;

Limayem et al., 2000).

2.1.3 Perceived Usefulness

Perceived usefulness refers to the degree to which a person believes that using a

particular system would enhance his or her job performance (Davis 1989). In the context

of online consumer behavior, Chen et al., (2002), Childers et al., (2001), and Heijden et

al.,(2001) found that perceived usefulness affects attitude toward online shopping.

Similarly, Chen et al., (2002), Gefen and Straub (2000), Heijden et al., (2001), and

Pavlou (2001) found perceived usefulness to be a significant factor affecting intention to

shop online.

18

2.1.4 Perceived Ease of Use

Perceived ease of use (PEOU) refers to the degree to which a person believes that

using a particular system would be free of effort (Davis, 1989). PEOU has received

enormous attention in the IT adoption studies. Chen et al., (2002), Childers et al., (2001)

and Heijden et al., (2001) found that PEOU influences attitudes toward online shopping.

2.1.5 Subjective Norm

Subjective norm refers to one’s perception of social pressure to perform or not to

perform the behavior under consideration (Athiyaman, 2002). The association between

subjective norms and behavioral intentions has been shown in several studies. For

example, studies in organization settings have shown that subjective norm is a crucial

determinant of behavioral intention (Davis, 1993). Hartwick and Barki (1994) also

suggested the effect of subjective norms to be more significant in the initial stages of

system implementation.

2.1.6 Perceived Behavioral Control

Perceived behavioral control refers to one’s perceptions about the ease or

difficulty in performing the behavior (Athiyaman, 2002). Perceived behavioral control is

important in explaining human behavior since an individual who has the intentions of

accomplishing a certain action may be unable to do so because his or her environment

prevents the act from being performed. In the context of online shopping, computer

access, Internet access, and availability of assistance are all behavioral control factors that

are important in facilitating online shopping behavior.

19

The influence of perceived behavioral control on the intention to shop online and

the actual shopping behavior has been widely considered in the area of online consumer

behavior. Most studies (Athiyaman, 2002; Limayem et al., 2000; Limayem et al., 2002,

Pavlou and Chai 2002; Skik and Limayem 2002, and Song and Zahedi 2001) found that

perceived behavioral control significantly affects intention to shop online. Limayem et

al., (2000) also found the link between perceived behavioral control and online shopping

to be significant.

2.1.7 Trust

Internet shopping is a new form of commercial activity, which tends to involve a

higher degree of uncertainty and risk when compared with traditional shopping. Internet

stores appear to be less well known to consumers, as they cannot physically examine the

quality of the products before making a purchase, nor can they fully monitor the safety

and security of sending sensitive personal and financial information through the Internet

to a party whose behaviors and motives may be hard to predict (Lee and Turban, 2001).

Thus, the concept of trust becomes very important in the context of online consumer

behavior. Trust refers to the confidence a person has in his or her favorable expectations

of what other people will do, based, in many cases, on previous interactions (Gefen,

2000). A significant number of studies (George, 2002; Heijden et al., 2001; Pavlou and

Chai 2002) found that trust is a salient determinant of online shopping attitude. Moreover,

Lynch et al., (2001) found that trust significantly affects a potential consumer’s intention

to shop online.

20

2.1.8 Internet Usage

Citrin et al., (2000) and Goldsmith (2002) found that consumers who are

proficient in the use of the Internet for means other than shopping will be more likely to

adopt the Internet for shopping. This link between Internet usage and online shopping

behavior is substantiated by Goldsmith and Goldsmith (2002) and Kwak et al., (2002).

2.1.9 Enjoyment

Enjoyment refers to the extent to which the activity of using the computer is

perceived to be enjoyable in its own right, apart from any performance consequences that

may be anticipated (Teo, 2001). The importance of enjoyment in online shopping has

been challenged in the past. Koufaris (2002) did not find any difference between non-

online buyers, occasional online buyers, and frequent online buyers. However, Goldsmith

and Goldsmith (2002) found enjoyment to be an important factor determining consumer

online shopping behavior.

2.1.10 Perceived Risk

Perceived risk refers to a consumer’s perceptions of uncertainty and adverse

consequences of buying from the web (Grazioli and Jarvenpaa 2000). Prior studies

(Heijden et al., 2001; Jarvenpaa and Todd 1996) found that perceived risk had a strong

impact on attitude. Moreover, Heijden et al., (2001), Pavlou (2001) and Tan and Teo

(2000) found that perceiver risk affects intention to shop online significantly. Similarly,

Miyazaki and Fernandez (2001) found perceived risk had a significant impact on online

purchasing behavior.

21

2.1.11 Experience

George (2002) and Goldsmith and Goldsmith (2002) argue that consumers who

have previous experience in online buying will be more likely to purchase online than

those who lack such experience. Hoffman et al., (1999) conclude that novice Internet

users are less likely to buy online. Further studies indicate that experience significantly

affects attitude toward online shopping and intention to shop online (French and O'Cass

2001,Vijayasarathy and Jones 2000). Thus experience is a significant determinant of

online shopping behavior (Eastin 2002, George 2002, Goldsmith and Goldsmith 2002).

2.1.12 Innovativeness

Innovativeness refers to the degree and speed of adoption of innovation by an

individual (Limayem et al., 2000). This construct has been of particular interest in

innovation diffusion research (Roger, 1995). Shopping on the Internet can be considered

as an innovative behavior because it is more likely to be adopted by innovators than non-

innovators. French and O’Cass (2001), Limeyem et al., (2000) and Limayem el al.,

(2002) found that innovativeness is a significant factor affecting attitude toward online

shopping. Further extensive research has shown that innovativeness is a significant

antecedent of intention to shop online (Goldsmith 2002, Limayem and Rowe 2001, Skik

and Limayem 2002) and that innovativeness is a significant factor of online shopping

behavior (Citrin et al., 2000, Goldsmith 2000, Goldsmith 2002, and Goldsmith and

Goldsmith 2002).

22

2.1.13 Habit

Triandis (1979) defines habit as situation-behavior sequences that have become

automatic and occur without self-instruction. It is a behavior tendency developed from

historical situations that an individual experienced in the past. Such tendency will then

elicit behavioral response from the individual automatically upon a stimulus which most

likely is a situation similar to the past. In the context of online consumer behavior, several

researchers found that habit affected attitudes to shop online (e.g., Frini and Limayem

2000, Limayem et al., 2000, Limayem and Rowe 2001). However, Frini and Limayem

2000, Limayem et al., 2000, and Limayem and Rowe 2001 found the link between habit

and intention to shop online to be statistically insignificant.

2.1.14 Perceived Consequences

According to Triandis (1979), each act or behavior is perceived as having a

potential outcome that can be either positive or negative. An individual’s choice of

behavior is based on the probability that an action will provoke a specific consequence.

Limayem et al., (2000), Limayem et al., (2002), and Limayem and Rowe (2001) found

that perceived consequences significantly affect an individual’s intention to shop online.

An individual may be favorable towards online shopping, but will not adopt it if he/she

perceives some important negative consequences. This view is consistent with the

technology acceptance model (Davis et al., 1989), which posits perceived usefulness as

an antecedent to both attitude and intentions.

23

2.1.15 Demographic Variables

Demographic variables include age, education, gender and income. Researchers

such as Case et al., (2001), Goldsmith and Goldsmith (2002) and Kwak et al., (2002)

found that age is not a significant determinant of online shopping behavior. Only Teo

(2001) found that age significantly affects online shopping behavior. Education is one of

the important demographic variables determining consumer buying online (Case et al.,

2001, Kwak et al., 2002). These studies argue that college students are the most active

group on the Internet. They argue that college students with considerable computer

knowledge are more likely to make online purchases than those with lesser knowledge.

A number of studies (e.g., Goldsmith and Goldsmith 2002, Kwak et al., 2002, and

Teo, 2001) found a significant impact of gender on online shopping behavior.Online

shopping has long been dominated by higher income consumers. Recent statistics,

however, show that purchases by lower and middle-income online users are on the

upswing. Case et al., (2001) and Kwak et al., (2002) found that income is an important

factor affecting online shopping behavior.

2.2 Theoretical Framework

This section of chapter two aims to give the reader a basic knowledge of adoption

theories. Since the thesis is based on the adoption theories, we believe that it is important

that the reader has basic knowledge of the adoption theories.

24

2.3 Adoption Theories

Shopping on the Internet is a voluntary individual behavior that can be explained

by behavioral theories such as the theory of reasoned action (TRA) proposed by Fishbein

and Ajzen (1975), theory of planned behavior (TPB) proposed by Ajzen (1991)

technology acceptance model (TAM) proposed by Davis(1986) , Triandis’ model

proposed by Triandis ( 1980) or diffusion of innovation theory (DOI) proposed by Rogers

( 1995).Among the theories mentioned the first three ones (TRA,TPB and TAM) have

been used more than the others in the IT adoption field .

Since TRA, TPB and TAM are the most popular theories employed to explain

online consumer behavior, hence in this paper we focus on these three adoption theories.

In this section of chapter two, we will review the Theory of reasoned action, theory of

planned behavior and technology acceptance model. Based upon these theories we

propose a model of online ticketing adoption.

2.3.1 Theory of Reasoned Action

the theory of reasoned action was introduced by Ajzen and Fishbein in 1975.The

theory of reasoned action regards a consumer’s behavior as determined by the

consumer’s behavioral intention, where behavioral intention is a function of ‘attitude

toward the behavior’ (i.e. the general feeling of favorableness or unfavorable ness for that

behavior) and ‘subjective norm (SN)’ (i.e. the perceived opinion of other people in

relation to the behavior in question) (Fishbein and Ajzen, 1975).The theory predicts

intention to perform a behavior by consumer’s attitude toward that behavior rather than

by consumer’s attitude toward a product or service.

25

Also, a consumer’s intention to perform a certain behavior may be influenced

by the normative social beliefs held by the consumer. As an example, a consumer might

have a very favorable attitude toward having a drink before dinner at a restaurant.

However, the intention to actually order the drink may be influenced by the consumer’s

beliefs about the appropriateness (i.e. the perceived social norm) of ordering a drink in

the current situation (with friends for a fun meal or on a job interview) and her/his

motivation to comply with those normative beliefs (Hawkins, et al., 2001).the theory of

reasoned action is depicted in figure 2.1.

Because of its achievement in developing a model to predict behavior, the Theory

of Reasoned Action has been the basis of researches and studies in a wide variety of

fields, including psychology, management, and marketing. One of the most important

topics in marketing research to which the theory can be applied is consumer behavior.

One of the most cited consumer behavior studies in which the Theory of Reasoned

Action played a central role was "The Theory of Reasoned Action: A Meta-Analysis of

Past Research with Recommendations for Modifications and Future Research by

Sheppard et al., 1988.

In the study, the effectiveness of the model proposed by Fishbein and Ajzen in

1975 was investigated. Two meta-analyses were conducted. The sample included 87

separate studies of the individuals' intentions and performance relationship and 87

separate studies of the individuals' attitudes and subjective norms and their intentions

relationship. The study concluded that "the model performed very well in the prediction

of goals and in forecasting activities involving an explicit choice among alternatives",

and that the predictive ability of the model was strong (Sheppard et al., 1988). Although

the study proved the effectiveness of the model developed by Ajzen and Fishbein (1980),

Sheppard et al., (1988) also found that the predictive ability of the Theory of Reasoned

Action is not valid if the behavior is not under full volitional control.

26

That is to say the theory of reasoned action is concerned with rational,

volitational, and systematic behavior (Fishbein and Ajzen, 1975), i.e. behaviors over

which the individual has control (Thompson, 1994).

Attitude toward the behavior

Subjective Norm

Intention

The person’s believe that the behavior leads to certain outcomes and his/her evaluations of these outcomes

The person’s believe that specific individuals or groups think he/she should or should not perform the behavior and his/her motivation to comply with the specific referents

Relative importance of attitudinal and normative considerations

Behavior

Figure 2.1: Theory of Reasoned Action

Source: Ajzen and Fishbein (1975)

This assumption has been widely criticized. Sheppard, Hartwick, and Warshaw

(1988) argue that researchers are often interested in situations in which the target

behavior is not completely under the consumer’s control. However, as observed by

Sheppard et al., “actions that are at least in part determined by factors beyond individuals

volitional control fall outside the boundary conditions established for the model”.

27

For example, a consumer may be prevented from buying groceries online if the

consumer perceives the purchase process as too complex or if the consumer does not

possess the resources necessary to perform the considered behavior. Such considerations

are incorporated into the theory of planned behavior (Ajzen, 1985, 1991).

2.3.2 Theory of Planned Behavior

The TPB (Ajzen, 1985) is a cognitive model of human behavior, in which the

central focus is the prediction and understanding of clearly defined behaviors. Theory of

planned behavior extends the theory of reasoned action to consider perceived behavioral

control for reflecting user perceptions regarding possible internal and external constraints

on behavior. According to Ajzen, the principal predictor of behavior is intention. People

tend to act in accordance with their intention to engage in a behavior. Intention can be

regarded as a motivation to engage in a particular behavior and represents an individual’s

expectancies about his/her behavior in a given setting.

Fishbein and Ajzen (1985) operationalzed Intention as the likelihood to act.

Intention is influenced by attitude, subjective norm, and perception of control over the

behavior. Attitude toward a particular act represents a person’s overall positive and

negative beliefs and evaluations of the behavior. In turn, attitude is derived from salient

behavioral beliefs of particular outcomes and evaluation of those outcomes. Subjective

norm is an individual’s perception of general social pressures from important others to

perform or not to perform a given behavior. It, in turn, is determined by an individual’s

normative beliefs and his/her motivation to comply with his/her referents. Lastly,

perceived behavioral control represents an individual’s perception of whether the

performance of the behavior is under one’s control; 'control’’ reflects whether the

behavior is, on the one hand, easily executed (control beliefs) and whether, on the other,

the required resources, opportunities, and specialized skills are available (perceived

control) (Conner and Abraham, 2001).

28

People are not likely to form a strong intention to perform a behavior if they

believe that they do not have any resources or opportunities to do so even if they hold

positive attitudes toward the behavior and believe that important others would approve of

the behavior. Theory of planned behavior is depicted in figure 2.2

Intention Behavior

Behavioral Beliefs & Outcome Evaluations

Attitude

Normative Beliefs & Motivations to Comply

Subjective

Norm

Control Beliefs & Perceived Facilitations

Perceived Behavioral

Control

Source: (Mathieson, 1991) Figure 2.2: Theory of Planned Behavior

TPB has been used in many different studies in the information systems literature

(e.g. Mathieson, 1991, Taylor and Todd 1995, Harrison et al., 1997).TRA and TPB have

also been the basis for several studies of internet purchasing behavior (George, 2002;

Javenpaa and Todd, 1997; Khalifa and Limayem 2003; Limayem et al., 2000; Pavilou,

2002; Song and Zahedi, 2001; Tan and Teo, 2000).

29

2.3.3 Technology Acceptance Model

Since the seventies, researchers have concentrated their efforts on identifying the

conditions or factors that could facilitate the integration of information systems into

business. Their search has produced a long list of factors that seem to influence the use of

technology (Bailey and Pearson, 1983).From the mid-eighties, IS researchers have

concentrated their efforts in developing and testing models that could help in predicting

system use. One of them, technology acceptance model (TAM) was proposed by Davis in

1989 in his doctoral thesis. Their model is an adaptation of the theory of reasoned action.

Attitude towards using (AT) and behavioral intention to use (BI) are common to TRA

and TAM, and Davis used Fishbein and Ajzen’s method to measure them. Davis chose

not to keep the variable subjective norms, because he estimated that it had negligible effect on BI. In TAM2, Venkatesh and Davis reconsidered this choice (Venkatesh, and

Davis, 2000).

The technology acceptance model (Davis 1989) is one of the most widely used

models of IT adoption. Since its introduction, the technology acceptance model (Davis

1989) has received considerable attention in the IT community. Recent studies suggest it

applies also to e-commerce and to the adoption of internet technology (Gefen and Straub,

2000).According to TAM, IT adoption is influenced by two perceptions: perceived

Usefulness and perceived ease- of- Use. Perceived usefulness is defined as “the degree to

which a person believes that using a particular system would increase his or her

performance”. Perceive ease of use, in contrast, refers to “the degree to which a person

believes that using a particular system would be free of effort” (Davis 1998).Two other

constructs in TAM are attitude towards use and behavioral intention to use. Attitude

towards Use is the user’s evaluation of the desirability of employing a particular

information systems application. Behavioral intention to use is a measure of the

likelihood a person will employ the application (Davis, 1989).

30

Tam’s dependent variable is actual usage. It has typically been a self-reported

measure of time or Frequency of employing the application. TAM postulates that external

variables intervene indirectly by influencing PEU and PU. There is no clear pattern with

respect to the choice of the external variables considered. these external variables include

factors such as Situational involvement, intrinsic involvement, prior use, argument of

change, Internal computing support, internal computing training, management support,

external computing, , external computing training, Role with regard to technology, tenure

in workforce, level of education, prior similar experiences, Participation in training, Tool

functionality, tool experience, task technology fit, task characteristics and etceteras. (Paul

Legris et al., 2003).Figure 2.3 shows the original TAM model based on Davis et al.,

1989)

Perceived Usefulness

Perceived Ease of Use

Attitude Behavior Intention

Actual Behavior

External Variables

Figure 2.3: Technology Acceptances Model

Source: (Davis et al., 1989)

31

Davis suggested that PEOU (perceived ease of use) has a positive, indirect effect

on system usage through PU (perceived usefulness). Empirical studies of TAM have

shown that usage of IS is determined by user behavioral intentions, which themselves are

jointly determined by User PU and attitudes toward using the IS (information system),

the last of which are jointly determined by user PU and PEOU. This also has a positive

but indirect effect on attitude through PU (Davis et al., 1989).

Many IS studies have been conducted based on the TAM, since PU and PEOU are

two general beliefs suited to predicting information systems usage. All relevant empirical

studies, such as the measurement of user acceptance of IT (Adams et al., 1992), and the

self-reported usage of IS (Szajna, 1996) have supported the hypothesis of TAM that PU is

directly related to IT/IS usage. Different from prior Studies (Chau, 1996; Gefen and Keil,

1998), Venkatesh and Davis (2000) have shown that PEOU has a positive, direct effect

on user acceptance of IT. However, no consistent conclusions have yet been reached

about the effect of PEOU on IS/IT usage.

Subsequent Research has expanded TAM in multiple directions. For example,

TAM2 examines the antecedents of perceived usefulness and incorporates the subjective

norm (i.e., social pressures related to adoption (Venkatesh, 2000). The impact of

computer self-efficacy, objective Usability, and experience with a system on perceived

ease of use is examined in (Venkatesh, 2000), whereas the antecedents of perceived ease

of use in terms of anchors (i.e., general beliefs about computers and computer usage) and

adjustments (beliefs shaped by direct experience with the target system) are examined in

(Venkatesh and Davis, 1996 ).

32

2.4. Differences Between Adoption Models

It’s maybe correct to say that evaluation and comparison of the different theories

reveals that they are not so different in terms of their differential predictions. Most

differences really amount to emphasis on one construct over another. Drawing upon the

theoretical foundation of TRA, Davis (1989) proposed that the theory be specially

modified for the domain of IT in form of a now widely accepted interpretation of IT

acceptance: the technology acceptance model (TAM).

In the TAM, as in the TRA, attitude predicts intention, and intentions predict

behavior. Unlike TRA, TAM does not include a subjective norm component as a

determinant of intention because of its uncertain theoretical ad empirical psychometric

status (Davis et al., 1989). Subjective norm can create the direct effects to norm on

intentions from indirect effects via attitude (Fishbein and Ajzen 1975). Comparing with

TRA, Technology Acceptance Model (TAM) is more oriented to analyze the human

behavior on using information System. TRA and TPB were formulated as generalization

of a wide area of individual behaviors, including the use of information technology.

In both theories Attitude is influenced by belief about the consequence of execute

the behavior weighted by the individual’s evaluation of each consequence. Depended

variable of interest in both theories is visible and both posit that behavior is influenced of

subjective norms. Attitude and intention have the same definition in both TAM and TPB.

Both theories predict behavior from intention. Mathieson (1991) also found TAM as a

quick and inexpensive in compare to TPB. Other suggestion about the differences is by

Mathieson (1991) found three main differences between TAM and TPB; their varying

degree of generality, TAM does not explicitly include any social variables, and finally the

models treat behavioral control differently.

33

2.5 Conceptual Model and Hypotheses

Based on the following reasons it was concluded that the TAM model is suitable

to identify the online ticketing adoption factors in our country (Iran), therefore it was

chosen to form the basis of the research model.

• TAM has been the most commonly employed model of IT usage (Taylor and Todd, 1995).

• Tam has received considerable empirical support (e.g., Davis, 1989; Davis et al.,

1989; Mathieson, 1991; Taylor and Todd, 1995).theses studies have found that TAM consistently explains a significant amount of variance (typically about 40 percent) in usage intention and behavior.

• It has been found that Tam’s ability to explain attitude toward using an

information system is better than other model’s (TRA and TPB) (Mathieson, 1991).

• Two belief factors of the TAM model (perceived ease of use and perceived

usefulness) are easy to understand and manipulate in information system design and implementation (Hung and Chang, 2004).

TAM is a very powerful and parsimonious model for explaining and predicting

much of the variance in new IT acceptance but it excludes the influence of social norms

and perceived behavioral control on behavioral intention. We believe that the proper

model for this research should include the social norm and behavioral control

factors.Subjective norm refers to one’s perception of social pressure to perform or not to

perform the behavior under consideration (Athiyaman, 2002). Considering the fact that

Iranian culture is more collectivist than individualist (Hofstede, 1980) and that

collectivists are more likely to comply with others than are individualists, we think that

the proper model of IT adoption for Iranian customers should include the subjective norm

construct. Furthermore, Hartwick and Barki (1994) suggested the effect of subjective

norms to be more significant in the initial stages of system implementation.

34

Since the online ticketing system has been developed recently so it is at the initial

stage of implementation and therefore we expect that subjective norm affect the intention

to use the online ticketing system. According to Ajzen (1991) the construct of perceived

behavioral control reflects beliefs regarding the availability of resources and

opportunities for performing the behavior as well as the existence of internal/external

factors that may impede the behavior. Perceived behavioral control is important in

explaining human behavior since an individual who has the intentions of accomplishing a

certain action may be unable to do so because his or her environment prevents the act

from being performed. In the context of online ticketing in Iran, computer access, Internet

access, Saman prepaid cards access and availability of assistance for passengers who

intend to purchase tickets online are all behavioral control factors that are important in

facilitating online ticketing behavior in our country.

That’s why we believe that the proper model for our research should include the

construct of perceived behavioral control as well. Such factors (perceived behavioral

control and subjective norm) have been found to have a significant influence on IT usage

behavior (e.g., Mathieson, 1991; Taylor and Todd, 1995 and Hartwick and Barki,

1994).these variables are also key determinants of behavior in the theory of planned

behavior (Ajzen, 1991), where social influences (subjective norm) are modeled as

determinants of behavioral intention, and perceived behavioral control is modeled as a

determinant of both intention and behavior. Hence it was concluded that adding

subjective norm (SN) and perceived behavioral control (PBC) to TAM would provide a

more complete test of the important determinants of IT adoption in general and online

ticketing adoption in specific. Buying tickets through internet in Iran is a new form of

commercial activity, which tends to involve a higher degree of uncertainty and risk when

compared with traditional way of buying tickets. Passengers who have got used in buying

tickets through traditional ways would have doubts in security of such system to do

online transactions and render trustworthy services.

35

This implies the concept of trust which has been found to be one of the most

important impediments of the online shopping. Trust refers to the confidence a person has

in his or her favorable expectations of what other people will do, based, in many cases, on

previous interactions (Gefen, 2000). A significant number of studies (George 2002,

Heijden et al., 2001, Jarvenpaa et al., 2000, Pavlou and Chai 2002) found that trust is a

salient determinant of online shopping attitude. Morever, Lynch et al., (2001) found that

trust significantly affects a potential consumers` intention to shop online. We believe that

adding the concept of trust to our model will improve the predictive ability of the model

to investigate the driving factors of online ticketing adoption in our country.

2.6 Pilot Study

To customize the research model and make sure that it is proper to identify the

main factors driving online ticketing adoption in Iran, it was necessary to be aware of

what the train passengers think about such factors, thereby, verifying if the proposed

model included such factors. For this purpose in depth interviews were conducted. A

depth interview is an unstructured, direct, personal interview in which a single respondent

is probed by an experienced interviewer to uncover underlying motivations, beliefs,

attitudes and feelings on a topic (Harris, 1996).

Seven interviews were conducted. The interviewees were those train passengers

who used the train frequently. The objective of the research was explained clearly for

each interviewee and since they were not familiar with the process through which they

could buy the train tickets online, complete information about know/how of the online

ticketing system was given.

36

Then the interviewees were asked about the main factors that affected their

intention to adopt online ticketing system to buy tickets thorough internet.the

interviewees verified the usefulness of buying tickets online but expressed their concerns

about factors such as lack of resources (internet, computer, Saman prepaid card) and

knowledge necessary for buying train tickets through internet and their disability to

purchase the tickets online by themselves and without any one else help. The respondents

verified the important role of mass media in informing the passengers about development

and availability of online ticketing system. They also emphasized on importance of

interacting with the system easily.

The other important factor that almost all of the interviewees mentioned was

regarding the perceived risk and lack of trust regarding the online transaction and the

quality of the services or products bought through internet. They simply compared this

new way of buying tickets with the traditional way of buying tickets and explained the

new way not trustworthy since they could not monitor security of the financial transaction

and quality of the service rendered. comparing the proposed model of this research with

the beliefs of customers, verifies the appropriateness of the proposed model for

investigating factors that influence the adoption of online ticketing in Iran.

The emerging model (shown in figure 2.4) was chosen as the research model of

this study. This study will not examine the intention-behavior relation since it is a cross

sectional research. Further, considering that internet ticketing in Iran is still relatively

new, it is reasonable for the present study to focus on the behavioral intentions to use

online ticketing system for purchasing the train tickets in Iran.

37

Attitude

Ease of Use

Perceived Usefulness

Intention

Subjective Norms

Perceived Behavioral Control

Trust

H4

H7

H5

H8

H2

H6

H1

H3

H9

Figure 2.4: Research model

2.7 Description of the Research Hypotheses

So far we reviewed the three main behavioral theories and with the help of them

we made the research model for online ticketing. In this part, we try to explain and

describe meaning of the hypothesized linked of the model in the context of online

ticketing.

38

2.7.1 Attitude

Attitude refers to one’s evaluation about the consequences of performing a

behavior (Athiyaman, 2002). In this research attitude represents passengers’ positive or

negative feelings about buying tickets through internet that affects the intention to buy

tickets online. As such, we suggest:

H5: There is positive relationship between Attitude towards buying tickets through

internet and intention to buy tickets through the internet

2.7.2 Perceived Usefulness

Perceived usefulness refers to the degree to which a person believes that using a

particular system would enhance his or her job performance (Davis 1989). In this study

perceived usefulness represents the degree to which train passengers believe in positive

consequences of using online ticketing system. As such we suggest:

H1: There is positive relationship between perceived usefulness of buying tickets

online and the attitudes to buy tickets online.

H4: There is positive relationship between perceived usefulness of buying tickets

online and the intention to buy tickets online.

2.7.3 Perceived Ease of Use

Perceived ease of use (PEOU) refers to the degree to which a person believes that

using a particular system would be free of effort (Davis 1989).

39

In this study perceived ease of use refers to the degree to which passengers

believe that using the online ticketing system for buying tickets through internet would be

easy and free of effort. Thus, we suggest:

H2: There is positive relationship between perceived ease of use of buying tickets

online and attitudes towards buying tickets online.

PEOU also has a positive but indirect effect on attitude through PU (Davis et al.,

1989).Thus:

H3: There is positive relationship between perceived ease of use of buying tickets

online and perceived usefulness of buying tickets online.

2.7.4 Subjective Norm

Subjective norm refers to one’s perception of social pressure to perform or not to

perform the behavior under consideration (Athiyaman, 2002). Considering the fact that

Iranian culture is more collectivist than individualist (hofstede, 1980) and that

collectivists are more likely to comply with others than are individualists, we expect that

Iranian train passengers under the influence of those referent groups (e.g. friends, family

members and …) that promote the idea of buying tickets through the internet will comply

with group norms and thereby intend to buy tickets through internet. Since the online

ticketing system has been developed recently so it is at the initial stage of implementation

and therefore we expect that subjective norm have a strong positive influence on the

intention to adopt online ticketing.

40

Hence, we suggest:

H6: There is a positive relationship between subjective norm and intention to

purchase the tickets through internet.

2.7.5 Perceived Behavioral Control

According to Ajzen (1991) the construct of perceived behavioral control reflects

beliefs regarding the availability of resources and opportunities for performing the

behavior as well as the existence of internal/external factors that may impede the

behavior. Hence, we agree with Taylor and Todd’s (1995) decomposition of perceived

behavioral control into” facilitating conditions” and the internal notion of individual

“self-efficacy”. Self efficacy indicates an individual’s self confidence in hi or her ability

to perform the behavior. In terms of internet purchasing, if an individual is self confident

about engaging in activities related to purchasing online, he or she should feel positive his

or her behavioral control (George, 2004).

Facilitating condition is defined as the degree to which an individual believes that

an organizational or technical infrastructure exists to support use of the system

(Venkatesh, 2003).Perceived behavioral control is important in explaining human

behavior since an individual who has the intentions of accomplishing a certain action may

be unable to do so because his or her environment prevents the act from being performed.

In the context of online ticketing in Iran, computer access, Internet access, Saman prepaid

cards access (facilitating conditions) and availability of assistance for passengers who

intend to purchase tickets online (self efficacy) are all behavioral control factors that are

important in facilitating online ticketing behavior in our country. Thus we suggest:

41

H7: There is a positive relationship between behavioral control and intention to

purchase the tickets through internet.

2.7.6 Trust

Internet shopping is a new form of commercial activity, which tends to involve a

higher degree of uncertainty and risk when compared with traditional shopping. Trust

refers to the confidence a person has in his or her favorable expectations of what other

people will do, based, in many cases, on previous interactions (Gefen, 2000). In this study

trust refers to the confidence passenger have in online transaction and consequences of

purchasing tickets through internet. Hence, we suggest:

H8: There is positive relationship between passengers` Trust in buying tickets

online and attitudes towards buying tickets online

H9: There is positive relationship between passengers` Trust in buying tickets

online and intention to buy tickets online

2.7.7 Behavioral Intention

Behavioral intention refers to “instructions that people give to themselves to

behave in certain ways” (Triandis, 1980). In our model, behavioral intention refers to

potential passengers’ intention to adopt online ticketing services. Considering that

internet ticketing in Iran is still relatively new, it is reasonable for the present study to

focus on the behavioral intentions to use online ticketing system for purchasing the train

tickets in Iran. Thereby, the link between intention and actual behavior is not tested in

this study. Summary of the research hypotheses are shown in table 2.2.

42

Table 2.2: Research Hypotheses

Table 2.2: Research Hypotheses

Hypotheses Description

H1 There is a positive relationship between perceived usefulness and attitude

H2 There is a positive relationship between perceived ease of use and attitude

H3 There is a positive relationship between perceived ease of use and perceived usefulness

H4 There is a positive relationship between perceived usefulness and intention

H5 There is a positive relationship between attitude and intention

H6 There is a positive relationship between subjective norm and intention

H7 There is a positive relationship between behavioral control and intention

H8 There is a positive relationship between trust and attitude

H9 There is a positive relationship between trust and intention

43

Chapter Three Research Methodology 3. Research Methodology In this chapter, we outline the methodology to be used in our research and the theoretical basis behind the approaches and their definitions for the understanding of the reader. We start by identifying the differences between the exploratory, descriptive, and exploratory research approaches and identify our research in this category. We also highlight the difference between deductive vs. inductive research, identify our research strategy. Data analysis methods and instruments are chosen and defined. 3.1 Research Purpose

Every researcher has his/her own personal motivation to perform a scientific study

while in general according to yin (1994), the types of research purpose can be classified

in three categories: exploratory research, descriptive research and explanatory (or casual)

research.

44

3.1.1 Exploratory Research

Exploratory research is characterized by its flexibility. When a problem is broad

and not specifically defined, the researches use exploratory research as a preliminary

step. By an exploratory research we mean a study of a new phenomenon exploratory

studies are a valuable means of finding" what is happening; to seek new insights; to ask

questions and to asses phenomenon in a new light (Yin 1994).Exploratory research has

the goal of formulating problems more precisely, clarifying concepts, gathering

explanations, gaining insight, eliminating impractical ideas, and forming hypothesis. It

can be performed using a literature research, surveying certain people about their

experiences, focus groups and case studies. For instance, when surveying people,

exploratory research studies would not try to acquire a representative sample, but rather,

seek to interview those who are acknowledgeable and who might be able to provide

insight concerning the relationship among variables. Case studies can include contrasting

situations or benchmarking against an organization known for its excellence. Exploratory

research may develop hypothesis, but it does not seek to test them.

3.1.2 Descriptive Research

When a particular phenomenon of a nature is under study, it is understandable

that, research is needed to describe it, to explain its properties and inner

relationships( Huczynski and Buchana 1991).the object of descriptive research is " to

portray an accurate profile of persons, events or situations (Robson , 1993). In academic

research, descriptive research is more rigid than exploratory research. When conducting a

management or business research, it seeks to describe users of a product or service,

determine the proportion of the population that uses a product or service, or predict future

demand for product or service.

45

As opposed to exploratory research, descriptive research should define questions,

people surveyed and the method of analysis prior to beginning of data collection. In other

words, the who, what, where, when, why and how aspects of the research should be

defined. Such preparation allows one the opportunity to make any required changes

before the process of data collection has begun. However, descriptive research should be

thought of as a means to an end rather than an end to itself.

Our research purpose and research questions reveal that this study is primarily

descriptive. Large -scale survey studies will be conducted to identify the main factors that

affect the Iranian passengers to buy the train tickets through the internet. The related data

will be collected and analyzed to verify the hypotheses of the research.

3.1.3 Explanatory Research

The study can be explanatory when the focus is on cause-effect relationships,

explaining what causes produces what effects (Yin 1994).explanatory (or causal) research

seeks to find cause and affect relationships between variables. It accomplishes this goal

through laboratory and field experiments.

3.2 Research Approach

In this part, we are going to find the right way to address the matter we focus on.

There are two main research approaches to choose from when conducting research in

social science: quantitative or qualitative method (yin, 1994).the most important

difference between the two approaches is to use the numbers and statistics you get the

choice of research approach naturally depends on the defined research problem and the

data needed for solving this problem.

46

Qualitative focus on the research that will have a better understanding of the

studies objects, they also have to be relative flexible. in addition, qualitative research is

the search for knowledge that is supposed to investigate, interpret and understand the

problem phenomenon by the means of an inside perspective ( Patel and Tebelius,

1987).the characteristics of qualitative studies are that they are based largely on the

researcher’s own description, emotions and reactions( yin, 1994). The qualitative

approach also includes a great closeness to the respondents or to the source that the data

are being collected from. Quantitative has a characteristic that tend to be more structured

and formalized. .the research tries to explain phenomenon with numbers to obtain results,

thereby basing the conclusion on the data that can be quantified. This approach is

especially useful when conducting a wide investigation that contains many units (Holme

and Solvang, 1995).

After comparing two research approaches, quantitative approach was chosen for

our thesis. The goal of this research is to identify the factors that influence the Iranian

passengers to purchase train tickets online .for doing so we have chosen a structured

framework. We have made a model by reviewing the related literature, thereby making

our research hypotheses. In fact we are trying to explain the online ticketing adoption

phenomenon with numbers, thereby basing our conclusion on the data that can be

quantified. We are going analyze the data collected from sample passengers and

generalize the data to the whole population. All the characteristics mentioned indicate

that the quantitative approach should be used in our research.

3.3 Deductive vs. Inductive

According to Saunders (2000), the research should use the inductive approach,

where the author would collect data and develop theory as a result of the data analysis;

47

While the deductive approach where the authors develop a theory and hypothesis

(or hypotheses) and design a research strategy to test the hypotheses. Deductive reasoning

works from the more general to the more specific. Sometimes this is informally called a

“top-down” approach; inductive reasoning works the other way, moving from specific

observations to border generalizations and theories. Informally, we sometimes call this

approach a “bottom-up” approach (Trochim 2002).

In this study begins with thinking up a proper research model about our topic of

interest (online ticketing adoption). Then we try to narrow that down into more specific

hypotheses that we can test. So we narrow down even further when we collect related

data to address the hypotheses. This ultimately leads us to be able to test the hypotheses

with specific data, resulting in confirmation or verification of our original theories. So we

draw on our research approach with deductive trait.

3.4 Research Strategy

There are three distinct conditions that will affect the choice of research strategy:

the type of research questions asked, the extent of control an investigator has over actual

behavioral events and the degree of focus on contemporary events.

According to Yin (1994) there are five different strategies for the research, of

course each one has both advantages and disadvantages. The five ones are an experiment,

a survey; history, an analysis of archival records and a case study. These are shown in

table 3.1

48

.

Table 3.1: Relevant Situations for Different Research Strategies

Research Strategy

Form of Research Question

Required Control Over Behavioral

Systems

Focus on Contemporary

Events

Experiment How, why Yes Yes

Survey Who, what, where,

how many, how much

No Yes

Archival Analysis Who, what, where,

how many, how much

No Yes/No

History How, why No No

Case Study How, why No No

Source: (Yin, 1994)

Since the aim of this study was to collect the answers from a large scale of

passengers who have not bought tickets online and formulate the main factors that affect

the intention to adopt online ticketing system, we have mainly chosen a survey as our

research strategy. This choice is partly determined by our research approach, which to

most extent is of quantitative nature. A survey is an appropriate strategy due to the fact

that the aim is to answer who, where, how many, or how much or what questions. There

is no faster, more affordable way to conduct a survey irrespective of size. Furthermore,

due to the quantitative nature of this study, a survey is appropriate because of its

quantitative character.

49

3.5 Defining the Target Population

Sampling design begins by specifying the target population. This is the collection

of elements or objects that possess the information sought by the researcher and about

which inferences are to be made (Malhotra and Briks 1999).Considering the fact that

online ticketing system is at its infancy stage in our country, and a trivial number of

passengers have used the system for buying tickets online, it was decided to target only

those passengers who had never used the system (inexperienced users of the system).

Since we were interested in the concept of intention, the fact that the respondents

are inexperienced users of the online ticketing system, does not disturb the result of this

study. Testing the behavioral models based on the data gathered from inexperienced users

is not something unusual and has been seen the literature review. Taylor and Todd in

1995, Conducted a study to assess the role of prior experience in assessing IT usage.

They tested the predictive ability of the Augmented TAM model based upon the data

gathered from two distinct groups of experienced and inexperienced users of the

computer resource center separately and compared the results to assess the role of

experience. Taylor and Todd (1995) encouraged the researchers to test:

1- Whether models such as TAM are predictive of behavior for inexperienced users of the information technology.

2- Whether the determinants of IT usage are the same for experienced and

inexperienced users of a system.

Furthermore, Yu et al., (2005), who conducted a study to verify TAM for to t-

commerce, used two distinct groups of samples of inexperienced and experienced users

of the t-commerce and compared the results. In an attempt to see if it’s possible to make a

comparison between experienced and inexperienced users on the online ticketing system

in Iran, we tried to take a sample from experienced users.

50

This was done with Raja Company cooperation, giving us the access to email

address of users of the system, but unfortunately the response rate was too low and the

size of the sample was too small to let us compare the results between two groups of

experienced and inexperienced users of the online ticketing system. Based on the

literature review, the current situation of online ticketing in Iran and the focus of study

which is on intention, it was decided to target the inexperienced users of the system.

Based on the above explanations we continue to define the target population of

this study. The target population should be defined in terms of elements, sampling units,

extent and time (Malhotra and Briks, 1999).An element is the object about which or from

which the information is desired. In survey research, the element is usually the

respondent. A sampling unit is an element, or a unit containing the element, that is

available for selection at some stage of the sampling process.

Extent refers to the geographical boundaries of the research and the time refers to

the period under consideration. . (Malhotra and Briks 1999) Raja passengers train

company has five main local traveling roots (Azarbayejan, Khorasan, Khozestan,

Golestan and Hormozgan) and three main international travelling roots (Tehran-Istambul,

Tehran-Damescue, Tehran-Van and Zahedan-Koveyte),

According to the explanations mentioned above, the target population of this

study is defined as:

-Elements: inexperienced users of the online ticketing system -Sampling units: trains traveling in the main traveling roots -Extent: trains traveling through the five main roots locally (inside Iran). -Time: 22 of the May 2005 to 23 June 2005.

51

3.6 Sampling Technique Selection

According to Saunders et al., (2000), sampling techniques can be divided into two

types:

• Probability or representative sampling • Non-probability or judgmental sampling

In probability sampling, sampling units are selected by chance. Probability

sampling is most commonly associated with survey-based research. This method of

sampling permits the researcher to make inferences or projections about the target

population from which the sample was drawn.

Non probability sampling relies on the personal judgment of the researcher

rather than on chance to select sample elements. Non probability samples may yield

good estimates of the population characteristics, but they do not allow for objective

evaluation of the precision of the sample results. (Malhotra and Briks 1999).since in

this study we want to generalize the results to the whole inexperienced passengers’

population, so the probability sampling method was chosen.

3.7 Questionnaire Development

In order to ensure that a comprehensive list of items was included, an extensive

review of previous work was conducted. To ensure reliability while operationalizing our

research constructs, we tried to choose those items that had been validated in previous

research. Table 3.2 shows the source of measures used for making questions. The

questionnaire consists of questions that relate to possible factors affecting adoption of

online ticketing system.

52

Likert five point scales ranging from “strongly agree” to “strongly disagree” were

used as a basis of questions. This scale has been used in previous e-commerce adoption

research.

Table 3.2: Research Variables and Measurements

Construct Source

Attitude Taylor and Todd (1995)

Intention Taylor and Todd (1995)

Perceived Ease of Use Davis (1989)

Perceived Usefulness Davis (1989)

Subjective Norm Taylor and Todd (1995)

Perceived Behavioral Control (self efficacy+ facilitating

conditions) Taylor and Todd (1995)

Trust Vijayasarathy (2004) and Jieun Yu et al.,(2005)

The questionnaire was translated to Farsi language .after translating the

questionnaire, a pilot study was conducted. At this stage 10 train passengers who had

never experienced using the online ticketing system, answered the questions these

passengers were asked to mention any ambiguity points in the questions.

53

With the help of the pilot study the original questionnaire was refined and some

corrections were made. A copy of the survey questionnaire is presented in Appendix B.

3.8 Data Collection

A survey was conducted to verify the research model. The sample was taken

randomly from inexperienced users of the online ticketing system in Iran. Inexperienced

users were defined as passengers who had never experienced purchasing the train tickets

through the internet. A team consisting of the university students who regularly traveled

with trains was made to distribute the questionnaires over the period 22 of the may 2005

to 23 June 2005. The purpose of the study was explained to the team members and they

were trained how to distribute the questionnaires and treat with the interviewees.

Since the respondents were inexperienced users who were not familiar with the

know-how of using the online ticketing system it was necessary to inform them about the requirements of using such system and the way it works. The interviewers were trained to

give a brief description of how the system works to respondents while showing them the

real web site pages one would see while interacting with the online ticketing system. The

respondents could see the process through which they could purchase tickets online, in

the lap top screens. After viewing the screens, the passengers were asked to answer the

questions. Total number of questionnaires distributed was equal to 600, from which 174

were incomplete and were excluded for analysis. This yields a response rate of 71%.this

means that the sample size of this study is equal to 426.

54

Chapter Four Data Analysis 4. Data Analysis In this chapter we will analyze the data collected based on the basis of frame of reference of this thesis. The partial least square method will be applied for analyzing the collected data. 4.1 Data Analysis Method

Analysis of the data was done by using the PLS (partial least squares method),

which is one of the SEM techniques. Structural Equation Modeling (SEM) techniques

such as Lisrel and Partial Least Squares (PLS) are second generation data techniques that

can be used to test the extent to which IS research meets recognized standards for high

quality statistical analysis (Gefen, 2000).SEM enables researchers to answer a set of

interrelated research questions in a single, systematic and comprehensive analysis by

modeling the relationships among multiple and dependent constructs simultaneously.

This capability for simultaneous analysis differs greatly from most first generation

regression models such as linear regression, ANOVA, and MANOVA, which can analyze

only one layer of linkages between independent and independent variable at a time.

55

(In appendix C comparison of the capability of these three approaches Lisrel, PLS

and Linear Regression is provided). Unlike First generation regression tools, SEM not

only assesses the Structural Model, the assumed causation among a set of dependent and

independent constructs, but in the same analysis, also evaluates the measurement model–

loadings of observed items (measurements) on their expected latent (constructs). The

result is a more rigorous analysis of the proposed research model and, very often, a better

methodological assessment tool (Gefen, 2000). (Summaries of the objective behind each

technique and limitations relating to sample size and distribution are provided in

appendix D).Due to the formative nature of some of the measures used and non normality

of the data, LISREL analysis was not appropriate for data analysis of this study (Chin and

Gopal, 1995). Thus, the Visual PLS 0.98 b software was chosen to perform the analysis.

4.2 Validity and Reliability

For reflective measures, all items are viewed as parallel measures capturing the

same construct of interests. Thus, the standard approach for evaluation, where all path

loadings from construct to measures are expected to be strong (i.e., 0.70 or higher), is

used (limayem et al., 2000). In the case of formative measures, all item measures can be

independent of one another since they are viewed as items that create the “emergent

factor.” Thus, high loadings are not necessarily true and reliability assessments such as

Cronbach’s alpha are not applicable. Under this situation, Chin (1998) suggests that the

weights of each item be used to assess how much it contributes to the overall factor. For

the reflective measures, rather than using Cronbach’s alpha, which represents a lower

bound estimate of internal consistency due to its assumption of equal weightings of items,

a better estimate can be gained using the composite reliability formula (Erlbaum Assoc.,

1998).All the measurements in this study are reflective, except the measurements of the

perceived behavioral control.

56

In this case the two concepts of “self efficacy” and “facilitating conditions’ form

the behavioral control concept. Thereby, behavioral control measurements are considered

to be formative. Table 4.1 provides information concerning the weights and loadings of

the measures to their respective constructs. The loadings of all the reflective measures of

this study area above 0.7, which indicates a good level of convergent validity.

Construct Indicator Loading Weight Attitude ATT1 -0.8164 ATT2 -0.9287 Intention INT1 -0.8621 INT2 -0.7401 PBC1 -0.6314 Perceived Behavioral PBC2 -0.5614 Control PBC3 -0.3361 PBC4 -0.3003 Subjective Norm SN1 -0.9022 SN2 -0.8324 PEOU1 0.9423 Perceived Ease of Use PEOU2 0.9457 PEOU3 0.939 PU1 -0.7199 Perceived Usefulness PU2 -0.8124 PU3 -0.8662 Trust TR1 -0.885 TR2 -0.957

Table 4.1: Weights and Loadings

57

Measurements of reliability for all scales are included in table 4.2. The composite

reliability was estimated to evaluate the internal consistency of the measurement model.

The composite reliability for all the constructs of this study was greater than the level of

0.60 which is recommended by Bagozzi and Yi (1995), as a good level for internal

consistency.

Table 4.2 : Composite Reliability

Construct Composite Reliability

Attitude 0.812559

Intention 0.783557

Perceived Behavioral Control 0.647661

Subjective Norm 0.804402

Perceived Ease Of Use 0.959642

Perceived Usefulness 0.843

Trust 0.918516

4.3 Results

The statistical significance of all the paths in the model was tested using the

bootstrap resampling procedure. (Cotterman and Senn, 1992).

58

Using one-tailed tests, eight of the nine paths were significant at p< 0.01 level,

providing support for H1, H2, H3, H5, H6, H7, H8 and H9. Figure 3.4 provides the results of

testing the structural links of the proposed research model using PLS analysis. These

results represent yet another confirmation of the appropriateness of the TAM for

explaining voluntary individual behavior of potential users of information technology

systems. The results also provide strong support for the new links added to the TAM

representing the effects of PBC, SN and Trust. In this part we try to explain the results in

the form of analyzing the antecedents of intention and attitude and perceived usefulness

with the help of statistical results.

4.3.1 Antecedents of Intention

As suggested by the t statistics and path coefficient values, subjective norm, trust,

attitudes toward online ticketing and perceived behavioral control had a positive

significant effect on intention to purchase tickets online whereas for perceived usefulness

the path was not found to be significant. The path between subjective norm and intention

was found to be significant (path coefficient= 0.44, p< 0.01), thereby supporting the

hypothesis 6.this is consistent with the findings of Taylor and Todd (1995) and Yu et al.,

(2004), who verified existence of a positive significant relationship between subjective

norm and the intention for inexperience users of the information technology systems.

The path between perceived behavioral control and intention was found to be

significant (path coefficient= 0.249, p< 0.01), thereby supporting the hypothesis 7.this is

consistent with the findings of Taylor and Todd (1995), who reported the existence of a

positive significant relationship between behavioral control and the intention for

inexperienced users of the information technology.

59

The path between attitude and intention was found to be significant (path

coefficient= 0.175, p< 0.01), thereby supporting the hypothesis 5.this is consistent with

the findings of Yu et al., (2004), who verified the existence of a positive significant

relationship between attitude and the intention for inexperienced users of the information

technology. But it is inconsistent with the findings of Taylor and Todd (1995), who

reported the existence of an insignificance relationship between attitude and the intention

for inexperienced users of the information technology.

0.020 t: 0.621

0.175 t: 3.616

0.119 t: 2.611

0.239 t: 3.636

0.227 t:4.116

R2: 0.40

R2: 0.372

R2: 0.052

BI A

T BC SN

PU

PEU0.074 t: 1.915

0.442 t :9.680 0.249

t :4.567

0.463 t: 7.141

A, attitude; PU, perceived usefulness; PEOU, perceived ease of use; SN, subjective norm; BC, perceived behavioral control, BI, behavioral intention; T, trust

Figure 4.1: Results of Testing the Hypothesized Links

60

The path between perceived usefulness and intention was not found to be

significant (path coefficient= 0.020, p> 0.05), thereby rejecting the hypothesis 4. This is

consistent with the findings of Yu et al., (2004), who verified the existence of an

insignificant relationship between perceived usefulness and the intention for

inexperienced users of the information technology. But it is inconsistent with the findings

of Taylor and Todd (1995), who reported the existence of a significance relationship

between perceived usefulness and the intention for inexperienced users of the information

technology. The path between trust and intention was found to be significant (path

coefficient= 0.074, p< 0.05), thereby supporting the hypothesis 9.this is inconsistent with

the findings of Yu et al., (2004), who reported the existence of an insignificance

relationship between trust and the intention for inexperienced users of the information

technology. The effects of the antecedents of intention accounted for 40% of the variance

in this variable. This is an indication of quite a good explanatory power of the model for

intention. The path coefficients showed that subjective norm was a more significant

determinant of intention relative to other determinants of intention. This shows the

importance of social influence in forming the potential users’ intention towards using the

online ticketing system.

4.3.2 Antecedents of Attitude

As suggested by the t statistics and path coefficient values, trust, perceived

usefulness and perceived ease of use had a positive significant effect on attitude towards

online ticketing. The path between trust and attitude was found to be significant (path

coefficient= 0.119, p< 0.01), thereby supporting the hypothesis 8.This is inconsistent with

the findings of Yu et al., (2004), who reported the existence of an insignificant

relationship between trust and attitude for inexperienced users of the information

technology systems. The path between perceived usefulness and attitude was found to be

significant (path coefficient= 0.239, p< 0.01), thereby supporting the hypothesis 1.

61

This is consistent with the findings of Taylor and Todd (1995) and Yu et al.,

(2004), who verified existence of a positive significant relationship between perceived

usefulness and attitude for inexperienced users of the information technology systems.

The path between perceived ease of use and attitude was found to be significant (path

coefficient= 0.463, p< 0.01), thereby supporting the hypothesis 2. This is inconsistent

with the findings of Yu et al., (2004), who verified the existence of an insignificant

relationship between perceived ease of use and the attitude for inexperienced users of the

information technology. But it is consistent with the findings of Taylor and Todd (1995),

who reported the existence of a significance relationship perceived ease of use and the

attitude for inexperienced users of the information technology. The effects of the three

antecedents of attitude (i.e., trust, perceived usefulness and perceived ease of use)

accounted for over 37% of the variance in this variable. This is an indication of the good

explanatory power of the model for attitude. Perceived ease of use had the strongest

effect with a path coefficient of 0.46 emphasizing the important role of ease of use in

driving attitude toward online ticketing. The results of the hypotheses tests are

summarized in table 4.3.

4.3.3 Antecedents of Perceived Usefulness

As suggested by the t statistics and path coefficient values, perceived ease of use

of online ticketing had a positive significant effect on perceived usefulness. Online

ticketing. The path between perceived ease of use and perceived usefulness was found to

be significant (path coefficient= 0.227, p< 0.01), thereby supporting the hypothesis 3.This

is consistent with the findings of Taylor and Todd (1995) and Yu et al., (2004), who

reported the existence of a significant relationship between perceived ease of use and

perceived usefulness for inexperienced users of the information technology systems.

Perceived ease of use accounted for over 0.05% of the variance in perceived usefulness.

62

Hypothesis Effects Structural Coefficient

t Statistic Remarks

H1 PU--->A 0.239 3.636* S

H2 EOU--->A 0.463 7.141* S

H3 EOU--->PU 0.227 4.116* S

H4 PU--->BI 0.02 0.621 N.S

H5 A--->BI 0.175 3.616* S

H6 SN--->BI 0.442 9.68* S

H7 PBC--->BI 0.249 4.567* S

H8 T--->A 0.119 2.611* S

H9 T--->BI 0.074 1.915** S

A, attitude; PU, perceived usefulness; PEOU, perceived ease of use; SN, subjective norm; PBC, perceived behavioral control, BI, behavioral intention; T, trust; S, supported, N.S; not supported *p<0.01 **p<0.05

Table 4.3: Results of the Hypotheses Tests

The effects of the three antecedents of attitude (i.e., trust, perceived usefulness

and perceived ease of use) accounted for over 37% of the variance in this variable. This is

an indication of the good explanatory power of the model for attitude. Perceived ease of

use had the strongest effect with a path coefficient of 0.46 emphasizing the important role

of ease of use in driving his/her attitude toward online ticketing.

63

Chapter Five Findings and Conclusions 5. Findings and Conclusions In this chapter, we are going to present what we have found from our research, so we could answer our research question. Furthermore, we will also give the conclusions of the research the innovative part of the research and what researchers can do for the future study. 5.1 Implications for the Theory

The aim of this research has been to increase the understanding of online

consumer behavior in Iran by answering the research question of this study. In this study,

we think we have contributed to the theory regarded applying existing theories

concerning online consumer behavior and verify their validity. Regarded our research

question, the majority of the findings for this study, supported the existing theories.

64

5.2 Innovative Part of the Research

This research is amongst the first studies in Iran to investigate the antecedents of

online shopping in general and online ticketing in specific. Considering the choice of

suitable research model we tried to be innovative and added the construct of trust to the

augmented technology acceptance model.

5.3 Discussions

The research shows that perhaps the most important factor influencing online

ticketing adoption is the subjective norm. Following the subjective norm, Perceived

behavioral control, attitude and trust were found to be other important determinants of the

online ticketing intention, respectively. The effect of subjective norm on intention was

even stronger than the effect of the perceived behavioral control, trust and attitude on

intention. This may be due to the fact that the online ticketing system has been introduced

recently in Iran; hence most of the passengers as well as referent groups that influence the

passengers` intention are not aware of existence o such a system.

This indicates the relative importance of the social influence on potential users

with no prior experience. This finding has implication for marketers. It indicates that

marketing tools such as advertisements in media or press play important roles in forming

the intention of the potential users of the online ticketing system. The online ticketing

system is developed recently in Iran. Thereby, most of the passengers were even not

aware that such a system exists! This suggests that efficient advertisement programs in

press and media about the online ticketing system would motivate passengers to use the

online ticketing system. Perceived behavioral control was found to be another important

antecedent of the online ticketing intention. This result was expected since passengers can

not use the online ticketing system if they don not have the resources and the knowledge

necessary for using the system.

65

The weights of facilitating condition was more than the weight of self efficacy (in

absolute terms), indicating the importance of facilitating condition as compared to self

efficacy. Passengers should have access to computer, internet and payment cards that can

be used for internet purchase. By the time being, the system only works with one type of

payment card (Saman prepaid cards), and other payment cards are not accepted. This is

one of the main obstacles hindering passengers from buying tickets online. Online

ticketing system should be flexible enough to allow the customers to use it with different

payment cards, thereby extending its reach. Information technology systems such as web

kiosks will solve the computer and internet access problems. These web kiosks shall be

located in main train stations, thereby giving comfortable access to the passengers. The

web kiosks should also be equipped with a toll free telephone number to give the

passengers the confidence and overcome possible confusions while using the system.

Perceived usefulness did not affect the intention to buy tickets online, directly. It had an

indirect effect on intention through attitude.

The relationship of trust with both attitude and intention was significant,

indicating the importance of trust in forming the attitude and intention of the

inexperienced users of the online ticketing system. This suggests that passengers will not

try the system unless they are assured of the security of the system in online transaction

process and trustworthiness of the tickets issued. there should be some kind of guarantee

(e.g. insurance), for such issues, so that customers are feel confidence about the security

of the system and thereby trust the online ticketing system and replace the traditional way

of buying tickets with the new online ticketing system .Attitude was found to have a

significant positive impact on intention. Among the antecedents of the attitude, perceived

ease of use had the strongest effect on attitude. Following the perceived ease of use,

perceived usefulness and trust had significance positive effect on attitude, respectively.

This suggests the important role of the perceived ease of use in online ticketing attitude

formation for the potential users of the online ticketing system. This has implications for

design and implementation of online ticketing systems.

66

Those specialists involved in designing the online ticketing systems should

design the systems with technical features and instructions that allow the novice users to

use them without being confused. Though being significant, the role of trust was not

found to be as important as the other two antecedents of attitude (perceived ease of use

and perceived usefulness). This means that concepts such as perceived ease of use and

perceived usefulness are more important than the concept of trust in forming the attitudes

of potential users of the online ticketing system towards using such a system. Marketers

involved in promoting usage of online ticketing system should target potential users with

designing special advertisement programs that focuses on ease of use, usefulness and

security of such systems, respectively, thereby affecting the attitude of these groups of

customers towards using online ticketing system.

5.4 Conclusions and Further Research

The conclusion of this study is not revolutionizing finding that could be summed

up in two or three point. However, many interesting aspects have been found. The

purpose of this study was to use and refine TAM in order to investigate factors that

motivate online ticketing adoption. The findings indicate that the TAM model is quite a

good predictor of behavior for inexperienced users of the information technology.

The results also showed strong support for the importance of considering the

concepts of subjective norm, perceived behavioral control and trust in adopting the online

ticketing system. In general, the result supports that the subjective norm, and later

perceived behavioral control, attitude and trust (respectively) are the most significant

factors that affect online ticketing adoption by inexperienced users. The results also had

implications for marketers of online ticketing systems.

67

This study, like all others, is not without its limitations:

1. Like most of the empirical studies in the online consumer behavior area, this

study is cross sectional. Therefore, it doesn’t capture the essence of the online shopping

phenomenon. Moreover many researchers emphasize the need for longitudinal studies to

better understand online shopping. Longitudinal studies allow the researchers to measure

both intention to buy and actual buying behavior. So researchers are encouraged to

conduct longitudinal researches about the online ticketing adoption factors in Iran to

better understand the essence of this area of study.

2-approximately 60% of the variance in the behavioral intention remains

unexplained. Future research should use more elaborate model in cooperating additional

antecedents factors beyond those mentioned in this study.

3-the respondents were selected randomly from passengers who had never

experienced buying tickets online. Given that we were interested in the perception of

intention to adopt, we were comfortable that these people were nonusers of the online

ticketing system. Another study can be conducted which specifically target people who

use online ticketing system. Even researchers can conduct studies to compare the

adoption factors between the experienced and non experienced users of the online

ticketing system.

4-this study asked respondents about the influence of referent groups in forming

their intention to use online ticketing system. Since subjective norm was found to be the

most important determinant of the intention, thereby, researchers are encouraged to

identify the identity of these referent groups that influence the passengers’ decision to use

online ticketing system.

5-this study concentrated on analyzing one service category (only tickets).this

could mean that result may suffer from lack of generalizability when other product or

service categories are considered. The result should be interpreted carefully when applied

to predict online shopping behavior in other product or service categories.

68

Reference List: 1. Adams, D. A., Nelson, R. R. and Todd, P. A. (1992) ‘Perceived Usefulness, Ease of

Use and Usage of Information Technology: A Replication’, MIS Quarterly, 16:2, 227-247.

2. Ajzen, I. (1985) ‘from intentions to actions: a theory of planned behavior’. In: Kuhl, J.,

Beskmann, J. (Eds.), Action Control: From Cognition to Behavior, Springer, New York, pp. 11–39.

3. Ajzen, I. (1991) ‘the theory of planned behavior: Some unresolved issues’,

Organizational Behavior and Human Decision Processes, 50, 179–211. 4. Aldridge, A.., Forcht, K. and Pierson, J. (1997) ‘Get linked or get lost: Marketing

strategy for the internet’, Internet Research, 7:3, 161–169. 5. Athiyaman, A. (2002), ‘Internet Users' Intention to Purchase Air Travel Online: An

Empirical Investigation’, Marketing Intelligence & Planning, 20, 4, 234-242. 6. Bagozzi, R and Yi, Y. (1988) ‘on the evaluation of structural equation models’,

journal of academy of marketing science, 16:1, 74-94. 7. Bailey, J. E. and Pearson, S. W. (1993) ‘Development of A Tool for Measuring and

Analyzing Computer User Satisfaction’, Management Science, 29: 5,530-545. 8. Bhatia, A. (2004) ‘E-Ticketing: The Cost-Effective Alternative’, travel & tourism

express, 15. 9. Burford, D. (1998). ‘Secure Electronic Ticketing on the Internet’, Department of

Computer Science, UCT. 10. Butler, P., and Peppard, J. (1998) ‘Consumer purchasing on the internet: Processes

and prospects’, European. Management Journal, 16:5, 600–610. 11.Case, T., Burns, O. M. and Dick, G. N. (2001) ‘ Drivers of on-Line Purchasing among

U.S. University Students’, in Proceedings of Americas Conference on Information Systems, Boston.

12. Chau, P. Y. K. (1996) ‘An Empirical Assessment of a Modified Technology

Acceptance Model’, Journal of Management Information Systems, 13:2, 185-204.

69

13. Chen, L..d., Gillenson, M. L., and Sherrell, D. L. (2002), ‘Enticting Online

Consumers: An Extended Technology Acceptance Perspective’, Information and Management, 39:8,705-719.

14. Childers, T. L., Carr, C. L., Peck, J., and Carson, S. (2001) ‘Hedonic and Utilitarian

Motivations for Online Retail Shopping Behavior’, Journal of Retailing, 77: 4, 511-535.

15. Chin, W.W. and A. Gopal. (1995) ‘Adoption intention in GSS: Importance of

beliefs’, Data Base Advance, 26:2, 42–64. 16. Chin, W. W. (1998) ‘The partial least squares approach for structural equation

modeling’, Lawrence Erlbaum Assoc., pp. 295–336. 17. Citrin, A. V., Sprott, D. E., Silverman, S. N., and Stem, D. E. (2000) ‘Adoption of

Internet Shopping: The Role of Consumer Innovativeness’, Industrial Management & Data Systems, 100:7, 294-300.

18. Conner, M., Abraham, C. (2001) ‘Conscientiousness and the theory of planned

behavior: toward a more complete model of the antecedents of intentions and behavior’, Personality and Social Psychology Bulletin, 27:11, 1547–1561.

19. Cotterman,W. and Senn. J.(1992) ‘ Challenges and Strategies for Research’ in

Information Systems Development. New York: Wiley Series in Information Systems.

20. Davis, F. (1993) ‘User acceptance of computer technology: System characteristics

user perceptions and behavior characteristics’, International Man-Machine Studies, 38, 475-487.

21 .Davis, F.D. (1989) ‘Perceived usefulness, perceived ease of use, and user acceptance

of information technology’, MIS Quarterly, 13:3,319-339. 22. Davis, F., Bagozzi, R. and Warshaw, P.R. (1989) ‘User acceptance of computer

technology’, Management science, 35:8, 982–1003. 23. Eastin, M. S. (2002) ‘Diffusion of E-Commerce: An Analysis of the Adoption of Four

E-Commerce Activities’, Telematics and Informatics, 19: 3, 251-267. 24. Engel, J. F., Blackwell, R. D. and Miniard, P. W. (1995), ‘Consumer Behavior’, the

daily press, Florida, 8th edition. 25. Fishbein, M. and Ajzen, I. (1975)’ Belief, Attitude, Intention and Behavior: An

Introduction to Theory and Research’, Reading, MA: Addison-Wesley

70

26. French, T. and O'Cass, A. (2001),’Internet Users' Adoption of Web Retailing: User

and Product Dimensions’, Journal of Product & Brand Management, 10:6, 361-381.

27. Frini, A., and Limayem, M. (2000) ’Factors Affecting Intentions to Buy through the

Web: A Comparative Study of Buyers and Non-Buyers’, in Proceedings of the 5th AIM Conference, Montpellier, France.

28. Gefen, D., and Keil, M. (1998) ‘Developer Trust-Building Behavior and User

Perceptions of Perceived Usefulness: A Social-Exchange Perspective’, Data Base for Advances in Information Systems, 29:2, 35-49.

29. Gefen, D., and Straub, D. W. (2000), ‘The Relative Importance Of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption’, Journal of

Association for Information Systems, 1, 1-10. 30. Gefen.D. (2000) ‘Structural equation modeling and regression: guidelines for research

practice’, Communication of the Association for information systems, Vol 4, article 7.

31. George, J. F. (2002) ‘Influences on the Intent to Make Internet Purchases’, Internet

Research-Electronic Networking Applications and Policy, 12: 2, 165-180. 32. Goldsmith, R. E. (2002), ‘Explaining and Predicting Consumer Intention to Purchase

over the Internet: An Exploratory Study’, Journal of Marketing Theory and Practice, 10:2, 22-28.

33. Grazioli, S., and Jarvenpaa, S. L. (2000) ‘Perils of Internet Fraud: An Empirical

Investigation of Deception and Trust with Experienced Internet Consumers’, IEEE Transactions on Systems Man and Cybernetics Part a-Systems and Humans, 30:4, 395-410.

34. Harris,L.M.(1996) ‘expanding horizons’marketing research’,magazine of

management and application ,8:2, 12 35. Harrison, D.A., Mykytyn,p.p. and Reimenschneider, C..K. (1997) ‘Executive

decisions about adoption of information technology in small business: Theory and empirical Tests’, Information systems research, 8:2, 171-95.

36. Hartwick, J. and Barki, H. (1994)’ Explaining the role of user participation in

information system use’, Management Science, 40:4, 440-465.

71

37.Heijden, V.H., Verhagen, T. and Creemers, M .(2001) ’predicting online purchase

behavior: replications and test of competing models’ , paper presented at the Proceedings of the 34th international conference on system sciences , Vrije University, Amsterdam, faculty of economics.

38. Hoffman, D. L., Novak, T. P. and Peralta, M. (1999)’Building Consumer Trust

Online’, Communications of the ACM, 42:4, 80-85. 39. Hofstede G. (1980) ‘Culture’s consequences’, Beverly Hills, CA: Sage 40. Huczynski and Buchanan (1991) organizational behavior-an introductory text, Hemel

Hempstead, UK, Prentice Hall 41. Hung, y- s. and Chang, M-C. (2004)’ user acceptance of WAP services: test of

competing theories’, computer standards and interfaces (available online at www.sciencedirect.com).

42. Iranian association of rail transport engineering (2005) ‘new year with new hopes’,

the Iranian association of rail transport engineering, Tehran, 4 March, p.1. 43. Jarvenpaa, S. L., and Todd, P. A. (1996) ‘Consumer Reactions to Electronic Shopping

on the World Wide Web’, International Journal of Electronic Commerce, 1:2, 59-88.

44. Jarvenppa, S.L. and Todd, P.A. (1997) ‘is there a future for retailing on the internet?’

in Peterson, R.A.(E.D.), Electronic Marketing and the Consumer, Sage, Thousand Oaks, CA,PP.139-54.

45. Jelassi, T. and Enders. A. (2005) ‘Strategies for e-business’, England, Pearson

Education 46. Kalakota, R., and Whinston. A.B. (1996) ‘Frontiers of Electronic Commerce’

Reading, MA: Addison-Wesley 47. Khalifa, M. and Limayem, M. (2003)’Drivers of internet shopping’, Communications

of the ACM, 46:12, 233-239. 48. Koufaris, M. (2002), ‘Applying the Technology Acceptance Model and Flow Theory

to Online Consumer Behavior’, Information Systems Research, 13:2, 205-223. 49. Kwak, H., Fox, R. J., and Zinkhan, G. M. (2002) ‘What Products Can Be

Successfully Promoted and Sold Via the Internet?’ Journal of Advertising Research, 42:1, 23-38.

72

50. Lawrence Erlbaum Assoc. (1998), ‘The partial least squares approach for structural

equation modeling’, pp. 295–336. 51. Lee, M. K. O., and Turban, E. (2001) ‘A Trust Model for Consumer Internet

Shopping’, International Journal of Electronic Commerce, 6: 1, 75-91. 52. Legris, P., Ingham, I. and Collerette, P. (2003) ‘Why do people use information

technology? A critical review of the technology acceptance model’, Information Management, 40: 3, 191-204.

53. Limayem, M., Khalifa, M., and Frini, A. (2000) ‘what makes consumers buy from

internet’, a longitudinal study of online shopping’, IEEE, 30:4. 54. Limayem, M. and Rowe, F. (2001) ‘Factors Affecting Intentions to Buy through the

Web in Hong Kong and in France: The Role of Social Uncertainty Avoidance’, in 5th International Conference on the Management of Networked Enterprises, Mahdia.

55. Limayem, M.,Cheung. MK.C. and Chan, WW.G. (2003) ‘a meta-analysis of online

consumer behavior: empirical research’, city university of Hong Kong. 56.Lynch, P. D., Kent, R. J., and Srinivasan, S. S. (2001) ‘The Global Internet Shopper:

Evidence from Shopping Tasks in Twelve Countries’, Journal of Advertising Research, 41:3, 15-23.

57. Malhotra, K.M. and Briks.F.D. (2003) Marketing Research, AN Applied Approach

(second edition), England, Pearson Education Ltd. 58.Mathieson, K.(1991) ‘ Predicting user intentions: Comparing the Technology

Acceptance Model with the Theory of Planned Behavior’, Information Systems Research, 2: 3 , 173-189.

59.Miyazaki, A. D., and Fernandez, A. (2001) ‘Consumer Perceptions of Privacy and

Security Risks for Online Shopping’, The Journal of Consumer Affairs, 35:1, 27-44.

60. Napier, H.A., Judd, P.J., Rivers O. N. & Wagner, S. W. (2001) ‘create A Winning E-

business’, Thomson learning, Boston, M.A. 61. Patel & Tebelius, U. (1994) ‘the basic of research methodology’, Lund university,

Student literature. 61. Pavlou, P. A., and Chai, L. (2002) ‘What Drives Electronic Commerce across

Cultures? A Cross-Cultural Empirical Investigation of the Theory of Planned Behavior’, Journal of Electronic Commerce Research, 3:4, 240-253.

73

62. Robson, C. (1993) ‘Real World Research: a resource for social scientists and

practitioner researchers’, Blackwell, Oxford. 63. Rogers, E.M. (1995)’diffusion of innovations’, New York free press, 4th edition. 64. Saunder .M. (2000) ‘research methods for business students’, Second edition,

financial times, prentice Hall 65. Song, J. and Zahedi, F. M. (2001) ‘Web Design in E-Commerce: A Theory and

Empirical Analysis’, in International Conference of Information Systems, pp.205-220.

66. Skik, M..and Limayem.M. (2002) ‘Intention to Buy from the Web: A Comparative

Study between Canada and Tunisia’, in Proceedings of the 5th AIM Conference, Hammamet, Tunisia.

67. Szajna, B. (1996) ‘Empirical Evaluation of the Revised Technology Acceptance

Model’, Management Science, 42:1, 85-92 68. Tan, M., and Teo, T. S. H. (2000) ‘Factors Influencing the Adoption of Internet

Banking’, Journal of Association for Information Systems, 1:1. 69. Taylor.S. and. Todd,P.A.(1995) ‘Understanding information technology usage: A test

of competing models’, Inf. Systems Research., 6:2, 144–176. 70. Taylor, S.and Todd, P.A. (1995), ‘Assessing IT usage: the role of prior experience’,

MIS Quarterly, 19:4,561-70. 71. Teo, T. S. H. (2001) ‘Demographic and Motivation Variables Associated with

Internet Usage Activities’, Internet Research: Electronic Networking Applications and Policy, 11:2, 125-137.

72. Thompson, R. L., Higgins, C. and Howell, J.M. (1994) ‘Personal computing: toward a

conceptual model of utilization’, MIS quarterly, 15:1,125-143. 73. Triandis, C. H. (1979) ‘Values, attitudes and interpersonal behavior’, Nebraska

Symposium on Motivation, Beliefs, Attitudes and Values, Lincoln, NE: Univ. 74. Venkatesh, V. and Davis, F.D. (1996) ‘A model of the Antecedents of Perceived Ease

of Use: Development and Test’, Decision Sciences, 27: 3, 451-481 75. Venkatesh, V. (2000) ‘Determinants of Perceived Ease of Use: Integrating Control,

Intrinsic Motivation, and Emotion into the Technology Acceptance Model’, Information Systems Research 11:4, 342-65.

74

76. Venkatesh. V., Morris. G.M., Davis. B. G. and Davis, D. F. (2003) ‘user acceptance

of information technology: towards a unified view’, MIS Quarterly, 27:3. 77. Vijayasarathy, L. R. and Jones, J. M. (2000) ‘Print and Internet Catalog Shopping:

Assessing Attitudes and Intentions’, Internet Research: Electronic Networking Applications and Policy, 10: 3, 191-202.

78. Vijayasarathy, R.L. (2004) ‘predicting consumer intentions to use online shopping: a

case for an augmented technology acceptance model’, Information & Management, 41, 747-762

79. Yan, G. and. Paradi, J.C. (1999) ‘Success criteria for financial institutions in

electronic commerce’ in Proc. 32nd Hawaii Int. Conf. System Sciences. 80. Yin, R.K. (1994)’ case study research: design & methods’, thousand oaks, sage

publication 81. Yu, J., Ha, I.., Choi, M. and Rho, J. (2005) ‘extending the TAM for a t- commerce’,

information and management, 14:2,126-130.

75

Appendix A: Acronyms AT: Attitude BI: Behavioral Intention EC: Electronic Commerce PBC: Perceived Behavioral Control PEOU: Perceived Ease of Use PU: Perceived Usefulness S: Supported SEM: Structure equation modeling SN: Subjective Norms NS: Not Supported T: Trust TAM: Technology Acceptance Model TPB: Theory of Planned Behavior TRA: Theory of Reasoned Action

76

Appendix B: Questionnaire Demographic questions: Gender: Male female Age: Less than 20 20-30 30-40 Above 40 Level of income (in Tomans ) Less than 100,000

100,000-150,000

150,000-200,000

200,000-250,000

More than 250,000

Educational level: Middle school High school University degree Advanced degree Seminary studies

77

Appendix B: Questionnaire (Continued) For each of the following, please answer by an x in the box that best represents your level of agreement or disagreement. Strongly Agree Neutral Disagree Strongly Agree Disagree 1 2 3 4 5 Attitude: It is a good idea to buy tickets through internet I like the idea of buying tickets through internet Intention: I intend to purchase tickets through Internet in the near future (i.e. next three months) It is likely that I will purchase tickets

through internet in the near future (i.e. next three months) Perceived Usefulness: Online ticketing system will enable Me to save time Online ticketing system will make It easier to buy tickets

Online ticketing system will enable

Me to buy tickets more quickly

78

Appendix B: Questionnaire (Continued) Strongly Agree Neutral Disagree Strongly Agree Disagree 1 2 3 4 5 Perceived ease of use:

I would become confused when I use the online ticketing system Learning to use the online ticketing

System would be easy for me Overall I would find online ticketing System easy to use

Subjective Norm: People who are important to me

Would think that I should buy Tickets through internet People who influence my behavior

Would think that I should buy tickets through internet Trust:

Making payments on the internet Is secure I think tickets purchased by using

The online ticketing system will be Trust worthy I can trust the online ticketing system to safeguard my privacy*

79

Appendix B: Questionnaire (Continued) Strongly Agree Neutral Disagree Strongly Agree Disagree 1 2 3 4 5 Facilitating conditions: I have the resources required to buy

Tickets through internet

I have knowledge and ability Necessary to buy tickets through Internet Self Efficacy:

I would feel comfortable buying Tickets through internet

I would be able to buy tickets Through internet even if there was

No one around to show me how to * indicates that the item was deleted because of its low loading

80

Appendix C: Comparative Analysis between Techniques

Table 1, Comparative Analysis between Techniques

LISREL PLS Linear Regression Issue

Objective of Overall Analysis

Show that the null hypothesis of the entire proposed model is plausible, while rejecting path-specific null hypotheses of no effect.

Reject a set of path-specific null hypotheses of no effect.

Reject a set of path-specific null hypotheses of no effect.

Objective of Variance Analysis

Overall model fit, such as insignificant chi-square or high AGFI.

Variance explanation (high R-square)

Variance explanation (high R-square)

Required Theory Base

Requires sound theory base. Supports confirmatory research.

Does not necessarily require sound theory base. Supports both exploratory and confirmatory research.

Does not necessarily require sound theory base. Supports both exploratory and confirmatory research.

Assumed Distribution

Multivariate normal, if estimation is through ML. Deviations from multivariate normal are supported with other estimation techniques.

Relatively robust to deviations from a multivariate distribution.

Relatively robust to deviations from a multivariate distribution, with established methods of handling non-multivariate distributions.

Required Minimal Sample Size

At least 100-150 cases.

At least 10 times the number of items in the most complex constructs.

Supports smaller sample sizes, although a sample of at least 30 is required.

Source: (Gefen, 2000)

81

Appendix D: Capability by Research Approach

Capabilities LISREL

Table 2: Capabilities by Research Approach

PLS Regression

Maps paths to many dependent (latent or observed) variables in the same research model and analyze all the paths simultaneously rather than one at a time.

Supported Supported Not supported

Maps specific and error variance of the observed variables into the research model.

Supported Not supported Not supported

Maps reflective observed variables Supported Supported Supported

Maps formative observed variables Not supported Supported Not supported

Permits rigorous analysis of all the variance components of each observed variable (common, specific, and error) as an integral part of assessing the structural model.

Supported Not supported Not supported

Allows setting of non-common variance of an observed variable to a given value in the research model.

Supported Not supported

Supported by adjusting the correlation matrix.

Analyzes all the paths, both measurement and structural, in one analysis.

Not supported Supported Supported

Not supported Can perform a confirmatory factor analysis Supported Supported

Provides a statistic to compare alternative confirmatory factor analyses models

Supported Not supported Not supported

Source: (Gefen, 2000)

82

83